Pairwise Feedback for Data Programming
arXiv:1912.07685
Abstract
The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning. The programmatic creation of labeled datasets via the synthesis of noisy heuristics provides a promising avenue to address this problem. We propose to improve modeling of latent class variables in the programmatic creation of labeled datasets by incorporating pairwise feedback into the process. We discuss the ease with which such pairwise feedback can be obtained or generated in many application domains. Our experiments show that even a small number of sources of pairwise feedback can substantially improve the quality of the posterior estimate of the latent class variable.
arXiv commentPresented at the NeurIPS 2019 workshop on Learning with Rich Experience: Integration of Learning Paradigms
References in corpus (5)
- Learning the Structure of Generative Models without Labeled Data
- Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
- Learning Dependency Structures for Weak Supervision Models
- Training Complex Models with Multi-Task Weak Supervision
- Multi-Resolution Weak Supervision for Sequential Data